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Related Experiment Video

Updated: Sep 9, 2025

Development of a Benchtop Model for Evaluating the Compatibility of Wound Dressing Materials with Negative Pressure Wound Therapy Systems
06:45

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Developing a Pressure Injury Predictive Indicator System for Data Mining in Health Care Information Systems: A

Chunxiang Qin1, Siqing Hu1, Jing Lu1

  • 1Health Management Medicine Center & Department of Nursing, Third Xiangya Hospital, Central South University.

Advances in Skin & Wound Care
|September 4, 2025
PubMed
Summary

This study developed a system to automatically predict hospital-acquired pressure injuries (PI) using electronic health records. The system identifies key indicators, improving patient care quality and reducing PI prevalence.

Keywords:
Delphi techniquemachine learningpredictive indicatorspressure injuryrandom foreststructured query language

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Area of Science:

  • Medical Informatics
  • Healthcare Quality Improvement
  • Clinical Data Mining

Background:

  • Hospital-acquired pressure injuries (PI) are a significant indicator of healthcare quality.
  • There is an increasing trend in the prevalence of hospital-acquired PIs.
  • Effective prediction of PIs is crucial for timely intervention.

Purpose of the Study:

  • To identify predictive indicators for potential hospital-acquired pressure injuries.
  • To develop a system for automatically mining these indicators from electronic medical record systems.

Main Methods:

  • A modified Delphi method was used for indicator development, involving expert interviews, literature review, and surveys.
  • Feature selection involved extracting indicators from the healthcare information system (HIS) using SQL.
  • The Random Forest technique was employed for indicator selection.

Main Results:

  • A predictive indicator system with 3 categories and 14 indicators was established.
  • High expert consensus was achieved for all indicators (agreement rate 83.30%-100.00%).
  • The automated prediction model demonstrated high accuracy (95.26%) with good agreement between manual and automatic extraction (Cohen κ 0.64-1.00).

Conclusions:

  • The developed predictive indicator system is suitable for automatic pressure injury prediction within HIS.
  • Further refinement and validation in real-world clinical settings are recommended.
  • This system has the potential to enhance patient care quality by enabling proactive PI prevention.